Data lakes versus data warehouses: choosing the right approach for big data analytics

The era of big data is evolving with the introduction of the data lake concept. While a data warehouse provides a well-structured model to manage big data, a data lake accepts data of any types and formats with or without schema and provides access to the data for diverse communities of users. A data lake provides flexible, agile, and scalable solution to manage the ever-increasing volume of big data we are witnessing in the world today, including many siloed data collected over the years by researchers through Arctic expeditions. In this paper, we present our conceptual model of a data lake for integrating the diverse huge amount of data collected by researchers during Arctic expedition. We also design a baseline metadata using a data-driven approach to manage the disparately huge structured, semi-structured, and unstructured data collected from the Arctic region. The resulting open data lake not only effectively manages big Arctic data but also supports machine learning on these big data.

With the advancement of modern technologies, a large amount of data generated from many users, devices, and applications is typical. This large amount of data is called Big Data. While the traditional approaches to preprocess, store, and analyze the data are based on data warehouse, preprocessing the massive scale of Big Data is somewhat costly in terms of computations and money. Hence, the alternative concept of Data Lakes originated, which can store raw data of any type. Both data warehouses and data lakes can be considered as methods of storing and processing Big Data. However, data lakes are often considered a panacea for Big Data problems. The main challenges of Big Data that can be solved by data lake, which are storing and processing, analyzing heterogeneous data sources, either structured, semi-structured, and unstructured. Also, data privacy can be considered with data lake models to ensure the data security and privacy part. Although the data lake provides an opportunity for business value by analyzing and predicting valuable information, this also results in cyber safety issues for many enterprises, including health care, defense, and finance. This chapter introduces data lakes with their components and associated challenges in processing and storing Big Data to address these problems. This chapter also presents the cyber safety and security issues relating to data lakes for the topmost targeted enterprises.

This paper examines the key similarities and differences between data lakes and data warehouses in relation to library analytics. With libraries beginning to embrace data-informed cultures, it is important to understand the potential benefits and challenges of each data architecture to select the best fit. Data lakes are known for the easy, scalable storage of unrelated, unstructured, and/or semi-structured data for analysis and machine learning applications. Data lakes are also capable of supporting real-time exploratory analytics and can merge different types of data, such as user interactions and content, as well as available data from social media. One of the challenges of a data lake is the requirement of knowledge and expertise on data governance, or the potential risk of becoming a "data swamp," otherwise known as unorganized data with no context or metadata. Conversely, data warehouses are a structured, optimized storage solution for clean, organized data. Data warehouses are ideal for some reporting solutions, tracking performance, and analyzing historical trends. They exceed query performance and reliability for everyday data functionalities but may lack flexibility for unstructured data or real-time analytics, the paper analyzes data warehouses and data lakes according to cost, scalability, governance, and usability, the analysis finds data lakes are more suited for libraries emphasizing innovation and research, while data warehouses remain prepared choices and practical implementation strategy for libraries emphasizing operational efficiency and standardized reporting. This comparison provides insights that assist library directors and decision-makers in aligning data and business intelligence strategies with institutional priorities and technological infrastructure.

In the era of data-driven decision-making, effective data management and datawarehousing are critical to the success of Management Information Systems (MIS). This reviewexplores recent advancements in data warehousing technologies and their transformative impacton MIS. Key topics include the fundamentals of data warehousing, advances in big data, clouddata warehousing, real-time processing, and in-memory databases. Through case studies fromdiverse industries, the review demonstrates how modern data warehouses enhance dataaccessibility, enable faster decision-making, and improve overall business performance. Thepaper also examines the challenges of managing large-scale data warehouses, such as securityand scalability, and considers future trends, including Artificial intelligence-driven datamanagement, data lakes, and edge computing. By analyzing these trends and technologies, thisreview highlights the evolving role of data warehousing in supporting MIS, ultimately enablingorganizations to maximize data value and drive strategic growth

Data is the driving force for the economy of any country. Data works as a fuel to the economy. The prime’s task for the organization is to store the data and use that data for the decision-making process. In the past, the organization used data warehouse and data marts to store the data and use for decision-making purpose, but for technology advancement, data warehouse faces many challenges and it fails to fulfill market demands. The biggest challenge for the data warehouse is to manage big data, data with velocity, data with huge volume, data with variety, data with veracity, and data with value. As of twenty-first century starts, worlds witness many new technologies like AI, deep learning, machine learning, and many more which all completely depend upon big data. Data warehouse fails to fulfill data engineer requirements to use these technologies to make decision-making system more effective. Data engineers want a new repository to store big data as data warehouse works on the concept of schema-on-write state that transforms the data before storage but engineers want data in raw format and later on according to business need they can transform the data to get the different values from data. To overcome the challenges which were faced by the data warehouse, research comes with a new concept known as data lake, a technologically advanced version of a data warehouse. Data lake works on the concept of schema-on-read. The objective of this chapter is to examine the idea of data lake from a user perspective as well as a technology perspective.

In the big data era, scientific and social data could complement each other for enhanced data analysis and scientific discovery. Such capabilities could be achieved by taking an infrastructure-based approach, compared to existing algorithm-based approaches. This paper investigates how scientific and social data could work together in a spatial data infrastructure (SDI) enabled by interoperable services. It takes a human-as-sensor perspective and treats the social data as a special kind of sensor data, which could be mined and used for event detection in the Sensor Web environment. Sensor Web, social data mining, and geoprocessing workflows are combined together for timely decision support from social and sensor data. The result is an SDI approach for big data analytics. A use case on haze-related data mining and analysis illustrates the applicability of the approach.

The volumes of data in Big Data, their variety and unstructured nature, have had researchers looking beyond the data warehouse. The data warehouse, among other features, requires mapping data to a schema upon ingest, an approach seen as inflexible for the massive variety of Big Data. The Data Lake is emerging as an alternate solution for storing data of widely divergent types and scales. Designed for high flexibility, the Data Lake follows a schema-on-read philosophy and data transformations are assumed to be performed within the Data Lake. During its lifecycle in a Data Lake, a data product may undergo numerous transformations performed by any number of Big Data processing engines leading to questions of traceability. In this paper we argue that provenance contributes to easier data management and traceability within a Data Lake infrastructure. We discuss the challenges in provenance integration in a Data Lake and propose a reference architecture to overcome the challenges. We evaluate our architecture through a prototype implementation built using our distributed provenance collection tools.

The media and telecommunications industries are undergoing a transformative evolution, driven by the convergence of technological advancements and shifting consumer behaviors. The rapid adoption of streaming services, 5G networks, and smart devices has led to an unprecedented surge in data generation. Each user interaction, from video streaming to mobile usage and network diagnostics, generates data that, when effectively harnessed, holds the potential to unlock significant business value. However, the sheer volume, complexity, and speed of data creation present formidable challenges for traditional data management systems. Data lakes and data warehouses have emerged as pivotal solutions for enabling robust Business Intelligence (BI) capabilities. A data lake serves as a vast reservoir capable of storing raw, unstructured, semi-structured, and structured data, offering businesses the flexibility to collect data from diverse sources without predefined schemas. In contrast, a data warehouse is a structured repository designed to store processed and organized data optimized for high-speed queries and analytical reporting. Together, these platforms create a holistic data ecosystem capable of supporting both exploratory and operational analytics. The successful integration of data lakes and warehouses empowers media and telecom companies to transition from reactive to proactive decision-making. By leveraging data-driven insights, these organizations can enhance customer experiences, optimize network performance, reduce operational costs, and unlock new revenue streams. This paper provides a comprehensive analysis of the strategic advantages of deploying data lakes and warehouses, outlines their integration methodologies, and examines their application in media and telecom business intelligence. Furthermore, it highlights the challenges faced in implementing these systems and offers insights into future trends that will shape the data management landscape in these industries.

Data is the biggest asset after people for businesses, and it is a new driver of the world economy. The volume of data that enterprises gather every day is growing rapidly. This kind of rapid growth of data in terms of volume, variety, and velocity is known as Big Data. Big Data is a challenge for enterprises, and the biggest challenge is how to store Big Data. In the past and some organizations currently, data warehouses are used to store Big Data. Enterprise data warehouses work on the concept of schema-on-write but Big Data analytics want data storage which works on the schema-on-read concept. To fulfill market demand, researchers are working on a new data repository system for Big Data storage known as a data lake. The data lake is defined as a data landing area for raw data from many sources. There is some confusion and questions which must be answered about data lakes. The objective of this article is to reduce the confusion and address some question about data lakes with the help of architecture.

Data quality is paramount in data-driven decision-making processes, especially when dealing with large volumes of data in environments like data warehouses and data lakes. These systems store vast amounts of raw and processed data from multiple sources, making data management and quality assurance complex yet critical. With the growing adoption of Artificial Intelligence (AI), new techniques and tools have emerged that can significantly enhance data quality. This paper discusses how AI can improve the quality of data within both data warehouses and data lakes by automating data cleansing, validation, anomaly detection, and ensuring consistency. It explores the benefits, challenges, and methodologies for integrating AI tools into these systems. Keywords: Data quality, AI in data quality, data warehouse, data lakes, big data, data processing, data cleansing, data profiling.

Data Lakehouse: A survey and experimental study

<p>Data lakes are becoming increasingly prevalent for Big Data management and data analytics. In contrast to traditional 'schema-on-write' approaches such as data warehouses, data lakes are repositories storing raw data in its original formats and providing a common access interface. Despite the strong interest raised from both academia and industry, there is a large body of ambiguity regarding the definition, functions and available technologies for data lakes. A complete, coherent picture of data lake challenges and solutions is still missing. This survey reviews the development, architectures, and systems of data lakes. We provide a comprehensive overview of research questions for designing and building data lakes. We classify the existing approaches and systems based on their provided functions for data lakes, which makes this survey a useful technical reference for designing, implementing and deploying data lakes. We hope that the thorough comparison of existing solutions and the discussion of open research challenges in this survey will motivate the future development of data lake research and practice.</p>

In big data analytics, the storage, processing, and analysis of large volumes of various types of data, including unstructured data, in their natural format is of great relevance. The article discusses aspects of the use of machine learning in the context of big data lake services. Modern organizations are increasingly using data lakes to store and manage large volumes of unstructured and structured data from various types of external data sources. Unlike traditional data warehouses, which require pre-processing and organization of data before storage, data lakes allow us to store big data in its own format, which provides unprecedented flexibility and scalability. This ability to support various types of data makes data lakes an important component for big data analytics, machine learning, and other advanced data processing applications. In addition, query optimization in data lakes, namely evolutionary optimization, is one of the key aspects of big data management, which uses adaptive approaches to query processing. The article also discusses a novel approach to machine learning in the context of evolutionary optimization.

With the increase in high volume, velocity, and variety of data, the traditional data analysis approaches are not adequate to handle diverse analysis challenges. Traditionally, a data warehouse is being used which is an integrated repository from various sources used for management and decision-making in business. Data is already in a transformed and structured format stored in a costly but reliable storage device. The data warehouse does not include all the data that may be not required at the time of construction of the data warehouse. With the advent of big data and to handle the data silos problem, the concept of Data Lake is introduced to handle data analysis. Data lakes have not replaced the data warehouse but rather complement it. In this chapter, firstly Data Lake is introduced and compared with predecessor technologies, then various tools and techniques are discussed to implement Data Lake.

Photo by Josh Riemer on Unsplash
 Introduction
 With the rapid advancements in neurotechnological machinery and improved analytical insights from machine learning in neuroscience, the availability of big brain data has increased tremendously. Neurological health research is done using digitized brain data.[1] There must be adequate data governance to secure the privacy of subjects participating in brain research and treatments. If not properly regulated, the research methods could lead to significant breaches of the subject’s autonomy and privacy. This paper will address the necessity for neuroprotection laws, which effectively govern the use of big brain data to ensure respect for patient privacy and autonomy.
 Background
 Artificial intelligence and machine learning can be integrated with neuroscience big brain data to drive research studies. This integrative technology allows patterns of electrical activity in neurons to be studied in detail.[2]Specifically, it uses a robotic system which can reason, plan, and exhibit biologically intelligent behavior. Machine learning is a method of computer programming where the code can adapt its behavior based on big brain data.[3] The big brain data is the collection of large amounts of information for the purpose of deciphering patterns through computer analysis using machine learning.[4] The information that these technologies provide is extensive enough to allow a researcher to read a patient’s mind. AI and machine learning technologies work by finding the underlying structure of brain data, which is then described by patterns known as latent factors, eventually resulting in an understanding of the brain’s temporal dynamics.[5]
 Through these technologies, researchers are able to decipher how the human brain computes its performances and thoughts. However, due to the extensive and complex nature of the data processed through AI and machine learning, researchers may gain access to personal information a patient may not wish to reveal. From a bioethical lens, tensions arise in the realm of patient autonomy. Patients are not able to control the transmission of data from their brains that is analyzed by researchers. Governing brain data through laws may enhance the extent of patient privacy in the case where brain data is being used through AI technologies.[6] A responsible approach to governing brain data would require a sophisticated legal structure.
 Analysis
 Impact on Patient Autonomy and Privacy 
 In research pertaining to big brain data, the consent forms do not fully cover the vast amounts of information that is collected. According to research, personal data has become the most sought out commodity to provide content to corporations and the web-based service industry. Unfortunately, data leaks that release private information frequently occur.[7] The storage of an individual’s data on technologies accessible on the internet during research studies makes it vulnerable to leaks, jeopardizing an individual’s privacy. These data leaks may cause the patient to be identified easily, as the degree of information provided by AI technologies are personalized and may be decoded through brain fingerprinting methods.[8]
 There has been an extensive growth in the development and use of AI. It is efficient in providing information to radiologists who diagnose various diseases including brain cancer and psychiatric disease, and AI assists in the delivery of telemedicine.[9] However, the ethical pitfall of reduced patient autonomy must be addressed by analyzing current AI technologies and creating more options for patient preference in how the data may be used. For instance, facial recognition technology[10] commonly used in health care produces more information than listed in common consent forms, threatening to undermine informed consent. Facial recognition software collects extensive data and may disclose more information than a person would prefer to provide despite being a useful tool for diagnosing medical and genetic conditions.[11] In addition, people may not be aware that their images are being used to generate more clinical data for other purposes. It is difficult to guarantee the data is anonymized. Consent requirements must include informing people about the complexity of the potential uses of the data; software developers should maximize patient privacy.[12] Furthermore, there is a “human element” in the use of AI technologies as medical providers control the use and the extent to which data is captured or accessed through the AI technologies.[13] People must understand the scope of the technology and have clear communication with the physician or health care provider about how the medical information will be used. 
 Existing Laws for Brain Data Governance 
 A strict system of defined legal responsibilities of medical providers will ensure a higher degree of patient privacy and autonomy when AI technologies and data from machine learning are used. Governing specific algorithmic data is crucial in safeguarding a patient’s privacy and developing a gold standard treatment protocol following the procurement of the information.[14] Certain AI technologies provide more data than others, and legal boundaries should be established to ensure strong performance, quality control, and scope for patient privacy and autonomy. For instance, currently AI technologies are being used in the realm of intensive neurological care. However, there is a significant level of patient uncertainty about how much control patients have over the data’s uses.[15] Calibrated legal and ethical standards will allow important brain data to be securely governed and monitored.
 Once brain signals are recorded and processed from one individual, the data may be merged with other data in Brain Computer Interface Technology (BCI).[16] To ensure a right and ability to retrieve personal data or pull it from the collection, specific regulations for varying types of data are needed.[17] The importance of consent and patient privacy must be considered through giving patients a transparent view of how brain data is governed.[18] The legal system must address discriminatory issues and risks to patients whose data is used in studies. Laws like the General Data Protection Regulation (GDPR) and the California Consumer Privacy Protection Act (CCPA) can serve as effective models to protect aggregated data. These laws govern consumer information and ensure the compliance when personal data is collected.[19] California voters recently approved expansion of the CCPA to health data. The Washington Privacy Act, which would have provided rights to access, change, and withdraw personal data, failed to pass. Other states should improve privacy as well,[20] although a federal bill would be preferable. Scientists at the Heidelberg Academy of Sciences argue for data security to be governed in a manner that balances patient privacy and autonomy with the commercial interests of researchers.[21] The balance could be achieved through privacy protections like those in the Washington Privacy Act. Although the Health Insurance Portability and Accountability Act (HIPAA) provides an overall framework to deter the likelihood of dangers to patient protection and privacy, more thorough laws are warranted to combat pervasive data transfer and analysis that technology has brought to the health care industry.[22] Breaches of patient privacy under current HIPAA regulations include releasing patient information to a reporter without their consent and sending HIV data to a patient’s employer without consent.[23] HIPAA does not cover information being shared with outside contractors who do not have an agreement with technology companies to keep patient data confidential. HIPAA regulations also do not always address blatant breaches on patient data confidentiality.[24] Patients must be provided with methods to monitor the data being analyzed to be able to view the extent of private information being generated via AI technologies. In health research, the medical purposes of better diagnosis, earlier detection of diseases, or prevention are ethical justifications for the use of the data if it was collected with permission, the person understood and approved the uses of the data, and the data was deidentified.
 A standard governance framework is required in providing the fairest system of care to patients who allow their brain data to be examined. Informed consent in the neuroscience field could reaffirm the privacy and autonomy of patients by ensuring that they understand the type of information collected. Laws also could protect data after a patient’s death. Malpractice in the scope of brain data could give people a cause of action critical in safeguarding patient’s rights. Data breach lawsuits will become common but generally do not cover deidentified data that becomes part of big data collection. A more synchronized approach to the collection and consent process will encourage an understanding of how big data is used to diagnose and treat patients. Some altruistic people may even be more likely to consent if they know the largescale data collection is helpful to treat and diagnose people. Others should have the ability to opt out of sharing neurological data, especially when there is not certainty surrounding deidentification.[25]
 Conclusion
 Artificial intelligence and machine learning technologies have the potential to aid in the diagnosis and treatment of people globally by extracting and aggregating brain data specific to individuals. However, the secure use of the data is necessary to build trust between care providers and patients, as well as in balancing the bioethical principles of beneficence and patient autonomy. We must ensure the highest quality of care to patients, while protecting their privacy, informed consent, and clinical trust. More sophis